Cancer is still an incurable disease, caused by the accumulation of somatic mutations. The recent technologies in high-throughput experimentation and next-generation sequencing have generated a huge amount of data, providing a unique opportunity to computationally challenge this deadly disease. This project will focus on the study of long noncoding RNAs in cancer. Particularly, we will use glioblastoma, an aggressive type of brain tumor as a proof-of-concept example to identify noncoding RNAs that might play important role in cancer progression.

Course type:

UROP1000 UROP1100 UROP2100 UROP3100 UROP4100

Applicant's Roles:

The applicants will develop software to identify noncoding RNAs from RNA-sequencing data.
Requirements: good programming skills, good mathematical background, and basic biology knowledge

Applicant's Learning Objectives:

Objective 1: to be able to analyze RNA-sequencing data with shell scripts;
Objective 2: to be able to perform statistical analyses to identify significant noncoding RNAs and illustrate the results;
Objective 3: to finish a project report by summarizing the studies.